• Korean Journal of Poultry Science
• /
• v.16 no.4
• /
• pp.201-207
• /
• 1989

This study was carried out to identify the chromosome morphological structure and G-, C-banding pattern of Korean native fowl. The samples used in this study were early chick embryos, and the method of chromosomal analysis quoted from the protocal of Ohio univ. with more or less modified. The results were summerized as follow as； 1. In each of macrochromosomal morphology, the arm-ratio, centromeric index, and relative length of Korean native fowl were more or less different from improved breeds, but the designations were the same. 2. The graphical pecks, by densitometric recordings, in each macrochromosome number of 1, 2, 3, 4, Z, and 5, numbered 21, 14, 12, 8, 11, and 4 in G-banded, and 16, 13, 9, 9, 9, and 4 in C-banded, respectively. Those pecks could be explained as a consequence of chromosome condensation during mitosis and of genetic material differences.

• Asian-Australasian Journal of Animal Sciences
• /
• v.15 no.3
• /
• pp.321-325
• /
• 2002

Karyotype analysis was carried out on blood samples of 30 water buffaloes belonging to different breed groups (i.e. Philippine Carabao (PC), Indian Murrah (IM), Bulgarian Murrah (BM), "$F_1$ 50% IM-50% PC", "$F_1$ 50% BM-50% PC" and "75% IM-25% PC"), using the modified Leucocyte Culture Technique. The modal chromosome numbers of the PC, "$F_1$ 50% IM-50% PC", "$F_1$ 50% BM-50% PC", IM, BM and "75% IM-25% PC" were 2n=48, 49, 49, 50, 50 and 50, respectively. The water buffalo chromosomes are mostly acrocentric (79.67%) and the remainder submetacentric (20.33%). Results of the ordinary least square analysis showed significant breed effects (p<0.01) on other karyotypic characteristics (i.e. relative length, arm ratio and centromeric index). Significant correlation between karyotypic characteristics and some animal performance traits were also found. The significant correlation values imply that karyotypic characteristics can be used as important criteria to select potentially productive young water buffaloes. In the future, more production and reproduction traits from non-institutional herds should be included in the analysis to reveal meaningful correlations with various karyotypic characteristics.

• Korean Journal of Poultry Science
• /
• v.13 no.2
• /
• pp.167-172
• /
• 1986

Chromosome size, number and shape were studied by the centromeric index, the arm ratio and the relative length of chromosome. The chromosomes of 50 early chick embryos which were derived from a pure line of Single Comb White Leghorns were examined. Using a colchicine, hypotonic treatment, fixation and air-drying technique, the clear prometaphase figures were obtained from the whole embryo. The results of the present investigation of chromosome pairs were as follows, 1. Pair 1 and 2; metacentric and submetacentric chromosomes which could be clearly distinguished from each other by size. 2. Pair 3 and 4: acrocentric chromosomes of similar length but the 4th pair had a distinct short arm which was not present in the 3rd. 3, Pair 5; metacentric sex chromosomes, 2 chromosome had relative 5th length but the W chromosome had slightly shorter length than 7th pair of chromosomes. 4. Pair 6; acrocentric chromosomes similar in shape to pair 3 but of little more than half the size. 5, Pair 7 and 8; acrocentric chrocentric but the 7th pairs had a definite short arm. 6. Pair 9; similar length to the 7, 8 pairs but had a medially placed centromere. 7. microchromosomes of 30 pairs ; nearly all acrocentric chromosomes which appeared as paired dots. The total number of diploid was appeared to 72-78. But a number of observations presented the total diploid number in 78 (58%). The inconstancy in number observed in this study was presumably due to the minute size of the microchromosomes. Thus, the modal numbers for the diploid chromosome was at least 78.

• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.197-200
• /
• 1995

In this paper, we proposed the multi-step multi-layer artificial neural network(MMANN) to classify the chromosome, Which is used as a chromosome pattern classifier after learning. We extracted three chromosome morphological feature parameters such as centromeric index, relative length ratio, and relative area ratio by means of preprocessing method from ten chromosome images. The feature parameters of five chromosome images were used to learn neural network and the rest of them were used to classify the chromosome images. The experiment results show that the chromosome classification error is reduced much more, comparing with less feature parameters than that of the other researchers.

• Korean Journal of Poultry Science
• /
• v.17 no.4
• /
• pp.269-274
• /
• 1990

Chromosome complements of Japanese quail (Coturnix coturnix japonica) were studied using several tissues which involving testis, leukocytes and embryos. The diploid count was estimated to be 2n＝78. Analyzing the metaphase of secondary meiosis in spermatocytes, the haploid count estimated to be n＝39. Morphometric analysis were studied by the centromeric index and relative length of 8 macro-chromosomes and Z, W chromosomes The differences of morphological feature were not significant among tissues. Exceptionally the chromosome 4 shelved a considerable variety in the presence of it's short arm.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.555-559
• /
• 1998

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis have been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We proposed an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted four morphological features parameters such as centromeric index (C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.). These Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results shown that the chromosome classification error was reduced much more than that of the other classification methods.

• Journal of Biomedical Engineering Research
• /
• v.17 no.4
• /
• pp.545-552
• /
• 1996

Researches on chromosome are very significant in cytogenetics since a gene of the chromosome controls revelation of the inheritance plasma The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, we propose an algorithm for reconstruction of the chromosDme image to improve the chromosome classification accuracy. Morphological feature parameters are extracted from the reconstructed chromosome images. The reconstruction method from chromosome image is the 32 direction line algorithm. We extract three morphological feature parameters, centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), by preprocessing ten human chromosDme images. The experimental results show that proposed algorithm is better than that of other researchers'comparing by feature parameter errors.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.05
• /
• pp.103-107
• /
• 1996

In this paper, We propose an algorithm for reconstitution of chromosome images to extract its morphological feature parameters. It is reconstituted from 460 chromosome images using the 32 direction line algorithm. We extract three morphological feature parameters such as centromeric index, relative length ratio, and relative area ratio. The experiment results show that our method is batter than that of other researchers comparing with the error of feature parameters.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.290-294
• /
• 1997

Chromosomes, as the genetic vehicles, provide the basic material for a large proportion of genetic investigations. The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, we propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We are employed three morphological feature parameters ; centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), as input in neural network by preprocessing twenty human chromosome images. The results of our experiments show that our TMANN classifier is much more useful in neural network learning and successful in chromosome classification than the other classification methods.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.6 s.44
• /
• pp.1-8
• /
• 2006

Many researchers have been studied for the automatic chromosome karyotype classification and analysis. For the automatic classify the each chromosome which is the image in microscope, it is necessary to process the sub-procedure, ie. image pre-processing, implementing karyotype classifier. The image pre-processing proceeded the each chromosome separation, the noise exception and the feature parameter extraction. The extracted morphological feature parameter were the centromeric index(C.I.), the relative length ratio(R.L.), and the relative area ratio(R.A.). In this paper, the fuzzy classifier was implemented for the human chromosome karyotype classification. The extracted morphological feature parameter were used in the input parameter of fuzzy classifier. We studied about the selection of the membership function for the optimal fuzzy classifier in each chromosome groups.